Methods for estimating reference signal received power of cellular communication signals

ABSTRACT

Methods for RSRP estimation in LTE networks that perform interference cancellation are provided. In particular, a bias that is present during interference cancellation is account for in the RSRP estimation of a target cell.

FIELD OF THE INVENTION

The invention relates generally wireless communications systems. Inparticular, the invention relates to estimating a Reference SignalReceived Power (RSRP) of a target cell in a Long Term Evolution (LTE)network by estimating and removing bias.

BACKGROUND OF THE INVENTION

In general, current cellular communications systems allow for cellulardevices to connect to high-speed data networks using radio waves as thetransmission medium. For example, Universal Mobile TelecommunicationsSystems (UMTS) can provide broadband applications, internet access,telephone access services, televisions service access and/or mobiletelephone services.

Communication over UMTS requires transmission over a radio spectrum,which is a medium that is shared between multiple technologies. In someinstances, these technologies can be interfering. Standards weredeveloped to, for example, ensure interoperability between equipmentfrom multiple vendors and aims to ensure that the allocated spectrum isused efficiently.

One such widely adopted standard is the 3^(rd) Generation PartnershipProject (3GPP) standard. The 3GPP standard has had many revisions,including an evolution into the Long-Term Evolution (LTE) standards. TheLTE standards also continue to evolve, such that there are multiplereleases, one of which is the LTE standards Release 11 (e.g., Rel-11).

In cellular communication, a user equipment (UE), for example, a mobilehandset, can be required to measure base station power and signalquality of neighboring base stations and report those measurements toits serving base station. These measurements can be used by networks to,for example, manage cell handover. These measurements can have a directimpact on the overall network capacity.

In LTE the UE can report two parameters: Reference Signal Received Power(RSRP) and Reference Signal Received Quality (RSRQ). These twoparameters can be measured using sequence of pilots that are typicallyunique per cell. The sequence of pilots is typically known as Cellspecific Reference Signal (CRS).

Currently, cellular communications systems environments are dense andcell coverage areas are small, as demand for cellular service grows.Denser cellular environments and smaller cells can reduce the accuracyof measuring RSRP and RSRQ, due to, for example increased interference.

Rel-11 of the LTE standards attempts to improve accuracy of measuringRSRP and RSRQ by requiring interference cancellation of CRS fromneighboring cells. One current method for interference cancellation ofCRS from neighboring cells includes performing a joint estimation of atarget cell's (e.g., the cell to be measured) channel response and aninterfering cell's channel response, followed by averaging of the targetcell channel energy over a measurement period.

One difficulty with this current method is that it can require a highcomputational power, due to, for example, filter coefficients used toestimate the channel response being dependent on a cross correlation ofthe target cell's and interfering cell's CRS patterns. The crosscorrelation can vary between orthogonal frequency-division multiplexing(OFDM) symbols and sub-frames, resulting in a matrix inversioncalculation for every OFDM symbol. The increase in computation powernecessary for joint estimation scan cause a UE to completely fail tofunction, cause a slowdown of other functions by the UE, the UE to needa bigger processing chip and/or quicker power loss.

Another current method of interference cancellation is serialinterference cancellation. In serial cancellation a dominant interferingcell channel is estimated, reconstructed, and subtracted from a receivedsignal. After the serial interference cancellation, the RSRP of thetarget cell can be estimated. One difficulty with serial interferencecancellation is that it can lead to an underestimation of the RSRP ofthe target cell due to, for example, failure of the dominant interferingcell channel estimation to account for a contribution of the target cellin the received communication signal. Without accounting for the targetcell, some of the energy of the target cell (e.g., the projection of theserving cell on the interference cell) is attributed to the interferingcell. Lack of accounting for the target cell can result in a power ofthe interfering cell being overestimated (e.g., due to a bias primarilycaused by the contribution of the target cell in the interfering cellchannel estimation), and thus underestimation of the target cell.

Therefore, it is desirable to perform interference cancellation whenestimating RSRP with a low computation power. It is also desirable toaccount for interference cancellation bias in the RSRP estimation.

SUMMARY OF THE INVENTION

One advantage of the invention is that it allows for interferencecancellation when estimating RSRP with a low computation power. Anotheradvantage of the invention is that it accounts for interferencecancellation bias when estimating RSRP.

In one aspect, the invention involves a method for Reference SignalReceived Power (RSRP) estimation of a target cell in Long Term Evolution(LTE) network. The method involves receiving a cellular communicationsignal transmitted over the LTE network. The method also involvesdetermining a first channel estimation of an interference cell of theLTE network based on the cellular communication signal. The method alsoinvolves performing an interference cancellation based on the cellularcommunication signal and the first channel estimation. The method alsoinvolves determining a second channel estimation of the target cellbased on the cellular communication signal after interferencecancellation. The method also involves determining a bias based on thesecond channel estimation. The method also involves determining a RSRPestimation of the target cell based on the second channel estimation andthe bias.

In some embodiments, the bias is a bias matrix and determining the biasfurther comprises determining an expectation value of the bias matrix.In some embodiments, the bias is a bias matrix and determining the biasfurther comprises using a Neumann series approximation. In someembodiments, the method involves determining the bias further comprisesretrieving the bias from a computer memory.

In some embodiments, the method involves determining the bias furthercomprises modifying the retrieved bias. In some embodiments, the methodinvolves determining the RSRP estimation is further based on a noisefactor. In some embodiments, the method involves retrieving the noisefactor from a computer memory. In some embodiments, the method involvesmodifying the retrieved noise factor.

In some embodiments, the pilot symbols of the target cell and pilotsymbols of the interfering cell overlap. In some embodiments, the targetcell and the interfering cell have an overlapping area of cellularcoverage. In some embodiments, the cellular communication signal is anorthogonal frequency-division multiplexing (OFDM) signal.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features, and advantages of the presentinvention, as well as the invention itself, will be more fullyunderstood from the following description of various embodiments, whenread together with the accompanying drawings.

FIG. 1 is a diagram of an exemplary cellular communications system,according to an illustrative embodiment of the invention.

FIG. 2 is a flow diagram illustrating a method for a Reference SignalReceived Power (RSRP) estimate of a target cell in Long Term Evolution(LTE) networks, according to an illustrative embodiment of theinvention.

FIG. 3a is a graph illustrating an RSRP measurement histogram, inaccordance with the prior art.

FIG. 3b is a graph illustrating an RSRP measurement histogram, inaccordance with an illustrative embodiment of the invention.

FIG. 4 is a graph illustrating average RSRP error versus Signal to NoiseRatio, in accordance with illustrative embodiments of the invention.

DETAILED DESCRIPTION

Generally, a user equipment (UE) receives a cellular communicationssignal from a target cell (e.g., first base station) and an interferencecell (e.g., a second base station) of a cellular communications LongTerm Evolution (LTE) network. The UE determines a first channelestimation of the interference cell based on the cellular communicationssignal. The UE performs an interference cancellation based on thecellular communication signal and the first channel estimation. The UEdetermines a second channel estimation of the target cell based on thecellular communication signal after interference cancellation. The UEdetermines a bias from the second channel estimation (e.g., the biascaused by contribution of the target cell in the interfering cellchannel estimation). The UE determines an RSRP estimate of the targetcell based on the second channel estimation and the bias.

FIG. 1 is a diagram of an exemplary cellular communications system 100,according to an illustrative embodiment of the invention. The cellularcommunications system 100 includes base stations 110 a, 110 b, 110 c, .. . , 110 n, generally base stations 110 and a user equipment (UE) 120.Each base station, 110 a, 110 b, 110 c, 110 n, has a correspondingcoverage area shown in FIG. 1 as cellular coverage area 125 a, 125 b,125 c, . . . , 125 n, respectively, generally cellular coverage areas125.

The cellular coverage areas 125 can overlap. The base stations 110 cantransmit cellular communications signals to the UE 120. The UE cantransmit cellular communication signals to the base stations 110.

The UE 120 is configured to receive cellular communication signals fromone or more of the base stations 110 when the UE 120 is within arespective cellular coverage area 125. The UE 120 is configured toreport a Reference Signal Received Power (RSRP) of one or more of thebase stations 125. The UE 120 can be configured according to LTEstandards. The UE 120 can be configured in accordance with any cellularcommunication standards as is known in the art.

The cellular communications system 100 can be a UMTS operating with theRel-11 LTE standards. The UE 120 can be a smart phone, a tablet device,a car phone, a computer, or any device capable of receivingcommunication signals over a UMTS network.

As is apparent to one of ordinary skill in the art, the base stations125 and their corresponding cellular coverage areas 125 are not to scaleand are illustrative only.

During operation, the UE 120 receives a request for an RSRP from one ofthe base stations 110, for example, base station 110 a. Base station 110a can be denoted as the target cell. In addition to being within thecellular coverage area 125 a covered by the target cell (e.g., basestation 110 a) the UE 120 is also within the cellular coverage areas 125b, 125 c, and 125 n, covered by base stations 110 b, 110 c, and 110 n,respectively. Cellular communication signals received by the UE 120 inthis overlapping coverage area can have contributions not only from thetarget cell (e.g., base station 110 a), but also from base stations 110b, 110 c, and 110 n, (e.g., interfering cells).

One of the interfering cells (e.g., base stations 110 b, 110 c, and 110n) can be a dominate interfering cell (e.g., base station 110 b), andthus its contribution to the cellular communication signals received bythe UE 120 can be cancelled from the cellular communications signalreceived by the UE 120. One dominant interfering cell or multipleinterfering cells can be provided to the UE 120 by the network. Uponreceipt of multiple interference cells, the UE 120 can perform anapproximate estimate of their power by, for example, using a correlationas is known in the art. Based on the correlation, the UE 120 candetermine which of the received interfering cells is dominant.

Ignoring the contribution of interference from the other interferingcells (e.g. 110 c, and 110 n) can cause an unwanted bias in the RSRPafter cancelling the interference from the dominate interfering cell 110a. The bias can cause the RSRP estimation of the target cell to beerroneous.

The RSRP estimation performed by the UE 120 can take into account thebias to provide an accurate RSRP measurement, despite the interferencecontribution of the other interference cells 110 c and 110 n.

As is apparent to one of ordinary skill in the art that FIG. 1 isexemplary only and that there are many configurations in a cellularnetwork that can cause a UE 120 to receive cellular communicationssignals with interference. For example, the cellular communicationsystems 100 can include any number of base stations and any number ofUE's as is known in the art. Size of the cell coverage area can varybased on properties of the base station.

FIG. 2 a flow diagram 200 illustrating a method for a Reference SignalReceived Power (RSRP) estimate of a target cell (e.g., base station 110a) in Long Term Evolution (LTE) networks, according to an illustrativeembodiment of the invention.

The method involves receiving (e.g., receiving by the UE 120, asdescribed above in FIG. 1) a cellular communication signal transmittedover the LTE network (e.g., cellular communications network 110, asdescribed above in FIG. 1). The cellular communication signal y_(i) canbe determined as follows:y _(i) =[y _(0,i) , . . . ,y _(N-1,i)]^(T)  EQN. 1

where N is a size of an observation vector of the cellular communicationsignal, and i is the i-th orthogonal frequency-division multiplexing(OFDM) symbol.

A Fast Fourier Transform (FFT) of the received cellular communicationsignal y_(i) in the i-th orthogonal frequency-division multiplexing(OFDM) can be follows:y _(i) =C _(0,i) F ₀ h _(0,i) +C _(1,i) F ₁ h _(1,i) +n _(i,)  EQN. 2

where C_(0,i) is a Cell specific Reference Signal (CRS) for the targetcell, F₀ is a modified version of the FFT for the target cell, andh_(0,i) is a channel domain impulse response sampled at a bandwidthsample rate of the cellular communications signal for the target cell,(C_(1,i) is a CRS for an interference cell of the LTE network, F₁ is amodified version of the FFT for the interference cell, and h_(1,i) is achannel domain impulse response sampled at the bandwidth sample rate ofthe cellular communications signal for the interference cell.

The method involves determining a first channel estimation {tilde over(h)}_(1,i) of an interference cell (e.g., interference cell 110 b, asdescribed above in FIG. 1) of the LTE network based on the cellularcommunication signal (Step 220). The first channel estimation {tildeover (h)}_(1,i) can be determined as follows:{tilde over (h)} _(1,i) =F ₁ ^(†) C _(1,i) ^(H) y _(i)  EQN. 3

where C_(1,i) ^(H) is the CRS for an interference cell of the LTEnetwork, H is a Hermitian transpose operator, and where F₁ ^(†) can bedetermined as follows:F ₁ ^(†)=(F ₁ ^(H) F ₁)⁻¹ F ₁ ^(H)  EQN. 4

The method also involves performing an interference cancellation Icbased on the cellular communication signal y_(i) and the first channelestimation {tilde over (h)}_(1,i) (Step 230). The interferencecancellation Ic can be determined as follows:Ic=y _(i) −C _(1,i) F ₁ {tilde over (h)} _(1,i)  EQN. 5

The method also involves determining a second channel estimation {tildeover (h)}_(0,i) of the target cell based on the cellular communicationsignal after interference cancellation (Step 240). The second channelestimation {tilde over (h)}_(0,i) can be determined as follows:{tilde over (h)} _(0,i) =F ₀ ^(†) C _(0,i) ^(H)(I _(c));  EQN. 6

The method also involves determining a bias based on the second channelestimation (Step 250). The bias can be a matrix. The bias matrix B_(i)can be determined as follows:{tilde over (h)} _(0,i) =h _(0,i) −F ₀ ^(†) C _(0,i) ^(H) C _(1,i) F ₁ F₁ ^(†) C _(1,i) ^(H) C _(0,i) F ₀ h _(0,i) +{dot over (n)} _(i)  EQN. 7F ₀ ^(†) is the F ₀ ^(†)=(F ₀ ^(H) F ₀)⁻¹ F ₀ ^(H)  EQN. 8ĥ _(0,i)=(I−B _(i))¹ {tilde over (h)} _(0,i)+(I−B _(i))⁻¹ {dot over (n)}_(i)  EQN. 9

where C_(0,i) ^(H) is the CRS for an interference cell of the LTEnetwork and {dot over (n)}_(i) is a filtered noise term.

The method also involves determining RSRP estimation f the target cellbased on the second channel estimation and the bias (Step 260). The RSRPestimation can be determined as follows:

$\begin{matrix}{{RSRP} = {{\frac{1}{M}{\sum_{i = 0}^{M - 1}{{F_{0}{\hat{h}}_{0,i}}}^{2}}} - {g\;\sigma^{2}}}} & {{EQN}.\mspace{14mu} 10}\end{matrix}$

where M is a predefined number of symbols in the cellular communicationsignal that, g is an expectation value of the filtered noise term {dotover (n)}_(i), and where σ² is noise power. The RSRP showing the biascan be expressed as follows:

$\begin{matrix}{{RSRP} = {{\frac{1}{M}{\sum_{i = 0}^{M - 1}{{{\overset{\sim}{h}}_{0,1}^{- H}\left( {I - B_{i}} \right)}^{- H}F_{0}^{H}{F_{0}\left( {I - B_{i}} \right)}^{- 1}{\overset{\sim}{h}}_{0,i}}}} - {g\;\sigma^{2}}}} & {{EQN}.\mspace{14mu} 11}\end{matrix}$

In some embodiments, determining the RSRP involves estimating (I−B_(i)).In some embodiments, estimating (I−B_(i)) reduces the computationalcomplexity of RSRP. In some embodiments, a Neumann series approximationis used to estimate (I−B_(i))⁻¹. In some embodiments, the Neumann seriesapproximation is used to estimate (I−B_(i))⁻¹ where lim_(k→∞)B^(k)=0. Insome embodiments, (I−B_(i))⁻¹ can be determined as follows:(I−B _(i))⁻¹=Σ_(k=0) ^(∞) B ^(k)  EQN. 12

In some embodiments, (I−B_(i))⁻¹ can be determined by summing the firsttwo elements of the series in EQN. 12, such that (I−B_(i))⁻¹ can bedetermined as follows:(I−B _(i))⁻¹ ≈I+B _(i)  EQN. 13

In some embodiments, the RSRP can be determined applying EQN. 13 to EQN.11, such that the RSRP can be determined as follows:

$\begin{matrix}{{RSRP} = {{\frac{1}{M}{\sum_{i = 0}^{M - 1}{\left( {{F_{0}^{H}F_{0}} + {2\;{{Re}\left( {B_{i}^{H}F_{0}^{H}F_{0}} \right)}}} \right){\overset{\sim}{h}}_{0,i}}}} - {g\;\sigma^{2}}}} & {{EQN}.\mspace{14mu} 14}\end{matrix}$

where Re(B_(i) ^(H)F₀ ^(H)F₀) denotes the real part of B_(i) ^(H)F₀^(H)F₀.

In some embodiments, B_(i) is determined based on its expectation, asfollows:BE(B _(i))=F ₀ ^(†)diag(F ₁ F ₁ ^(†))F ₀  EQN. 15

where diag denotes the diagonal matrix with its main diagonal equal tothe main diagonal of F₁F₁ ^(†). The expectation found in EQN. 15 is theexpectation over the CRS patterns cross correction for the case wherethere is an independent identical distribution of the CRS.

In some embodiments, the RSRP can be determined based on an expectationof a contribution of bias to the RSRP. In this manner, a contribution ofbias to the computation complexity of the RSRP determination can benegligible, in comparison to a typical computation complexity fordetermining RSRP that does not account for bias. In some embodiments,the RSRP can be determined by applying the expectation value in EQN. 15to EQN. 13, such that the RSRP can be determined as follows:

$\begin{matrix}{{RSRP} = {{\frac{1}{M}{\sum_{i = 0}^{M - 1}{\left( {{F_{0}^{H}F_{0}} + {2\;{{Re}\left( {{\overset{\_}{B}}^{H}F_{0}^{H}F_{0}} \right)}}} \right){\overset{\sim}{h}}_{0,i}}}} - {g\;\sigma^{2}}}} & {{EQN}.\mspace{14mu} 16}\end{matrix}$

where B_(i) ^(H)F₀ ^(H)F₀B_(i) in EQN. 13 is ignored, due to, forexample, B being relatively small and therefore its squared termnegligible.

In some embodiments, F₀ ^(H)F₀+2Re(B ^(H)F₀ ^(H)F₀) of EQN. 16 isdetermined off-line. In some embodiments, a value for F₀ ^(H)F₀+2Re(B^(H)F₀ ^(H)F₀) is stored in computer memory and retrieved during theRSRP determination. In some embodiment, a value for F₀ ^(H)F₀+2Re(B^(H)F₀ ^(H)F₀) is stored in a lookup table.

In some embodiments, the value for F₀ ^(H)F₀+2Re(B ^(H)F₀ ^(H)F₀) ofEQN. 16 is further modified upon retrieval from memory. In someembodiments, the value for F₀ ^(H)F₀+2Re(B ^(H)F₀ ^(H)F₀) of EQN. 16 ismodified in according to corresponding channel tap positions and/oraccording to a delay spread of the channel. In some embodiments, thevalue for F₀+2Re(B ^(H)F₀ ^(H)F₀) of EQN. 16 is modified with a lowrate:

In some embodiments, the expectation value of the filtered noise term{dot over (n)}_(i) is determined as follows:g=Σ _(i=0) ^(M-1) Tr(D _(i) ^(H) G _(i) D _(i))  EQN. 20

where D can be determined as follows:D=F ₀ ^(†) C _(0,i) ^(H)(I−C _(1,i) F ₁ F ₁ ^(†) C _(1,i) ^(H))  EQN. 21

where G_(i) can be determined as follows:G _(i) =F ₀ ^(H) F ₀+2Re(F ₀ ^(H) F ₀ B _(i))  EQN. 22

In some embodiments, expectation value of the filtered noise term can bedetermined based on an expectation of a contribution of bias to filterednoise. In this manner, a contribution of bias to the computationcomplexity of the filtered noise (and thus to the RSRP) determinationcan be negligible, in comparison to a typical computation complexity fordetermining the filtered noise (and thus the RSRP) without accountingfor bias. In some embodiments, the filtered noise can be determined byapplying the expectation value in EQN. 15 to EQN. 12, such that thefiltered noise can be determined as follows:g=M·Tr(F ₀ ^(†H) GF ₀ ^(†)−2Re(F ₀ ^(†H) GF ₀ ^(†)diag(F ₁ F ₁^(†)))+M·Tr((F ₁ F ₁ ^(†))^(H)diag(F ₀ ^(†H) GF ₀ ^(†))F ₁ F ₁^(†))  EQN. 23

where G can be determined as follows:G=F ₀ ^(H) F ₀+2Re( B ^(H) F ₀ ^(H) F ₀)  EQN. 24

In some embodiments, the determination of EQN. 24 and 24 is determinedoff-line. In some embodiments, the value for EQN. 23 is stored incomputer memory and retrieved during the RSRP determination. In someembodiments, a value for EQN. 23 is stored in a lookup table.

In some embodiments, the value for EQN. 24 is further modified uponretrieval from memory. In some embodiments, the value for EQN. 24 ismodified based upon channel tap positions, in accordance with a delayspread of the channel, or any combination thereof. In some embodiments,the value for EQN. 24 is modified in low rate.

FIG. 3a is a graph 300 illustrating an RSRP measurement histogram, inaccordance with the prior art. FIG. 3b is a graph 350 illustrating anRSRP measurement histogram, in accordance with an illustrativeembodiment of the invention. In FIG. 3a and FIG. 3b , an absolute errorvalue of the RSRP is presented over 602 cell identity combinations. Forthe examples of FIG. 3a and FIG. 3b , the target and interfering cellshave colliding CRS, the interfering cell timing is delayed by 3 microsecond compared to the target cell, and the channel estimation filtersuses 12 taps per estimated sub-carrier. In addition, a single CRS portis transmitted by each cell, signals propagate through 2 AWGN channelsand are received with 2 RX antennas at the UE. Estimated RSRP valuesfrom 10 sub-frames, 2 RX antennas and 4 OFDM symbols per measuredsub-frame are non-coherently averaged to calculate RSRP for ameasurement period of 200 ms. Relative powers of the interfering cell,target cell and AWGN are 0 dB, −4 dB, −6 dB, respectively.

In FIG. 3a the absolute error value of the RSRP are shown where the RSRPis determined without accounting for bias. The error values in FIG. 3arange from approximately 0.5 dB to approximately 3.5 dB with an averageof approximately 1.9 dB. In FIG. 3b the absolute error value of the RSRPare shown where the RSRP is determined accounting for bias, for example,using the method described in FIG. 2 above. The error values in FIG. 3brange from approximately 0.2 dB to 2.0 dB with an average error of 0.6dB.

FIG. 4 is a graph illustrating average RSRP error versus Signal to NoiseRatio, in accordance with illustrative embodiments of the invention. Inparticular, FIG. 4 shows the RSRP measurement error for a RSRPmeasurement where interference cancellation is not performed, a RSRPmeasurement where interference cancellation is performed but the RSRPdetermination does not account for bias, and a RSRP measurement whereinterference cancellation is performed and the RSRP determination doesaccount for bias (e.g., the RSRP as determined above in accordance withFIG. 2), for two different target/interference cell combinations. As canbe seen in FIG. 4, for both of the target/interference cellcombinations, the average RSRP error is significantly improved wheninterference cancellation is performed and the RSRP determination takesthe bias into account.

Method steps can be performed by one or more programmable processors(e.g., on a mobile device) executing a computer program to performfunctions of the invention by operating on input data and generatingoutput. Method steps can also be performed by an apparatus and can beimplemented as special purpose logic circuitry. The circuitry ca forexample, be a FPGA (field programmable gate array) and/or an ASIC(application-specific integrated circuit). Modules, subroutines, andsoftware agents can refer to portions of the computer program, theprocessor, the special circuitry, software, and/or hardware thatimplement that functionality.

The User Equipment can include any computing device, for example, acomputer, a computer with a browser device, an IP phone, a mobile device(e.g., cellular phone, personal digital assistant (PDA) device, laptopcomputer, electronic mail device), and/or other communication devices.The computing device can be, for example, one or more computer servers.The computer servers can be, for example, part of a server farm.

The computer storage can be, for example, a random access memory (RAM)module, a read only memory (ROM) module, a computer hard drive, a memorycard (e.g., universal serial bus (USB) flash drive, a secure digital(SD) flash card), a floppy disk, and/or any other data storage device.Information stored on a storage module can be maintained, for example,in a database (e.g., relational database system, flat database system)and/or any other logical information storage mechanism.

The above described networks can be implemented in a packet-basednetwork, a circuit-based network, and/or a combination of a packet-basednetwork and a circuit-based network. Packet-based networks can include,for example, the Internet, carrier internet protocol (IP) network (e.g.,local area network (LAN), wide area network (WAN), campus area network(CAN), metropolitan area network (MAN), home area network (HAN), aprivate IP network, an IP private branch exchange (IPBX), a wirelessnetwork (e.g., radio access network (RAN), 802.11 network, 802.16network, general packet radio service (GPRS) network, HiperLAN), and/orother packet-based networks. Circuit-based networks can include, forexample, the public switched telephone network (PSTN), a private branchexchange (PBX), a wireless network (e.g., RAN, Bluetooth®, code-divisionmultiple access (CDMA) network, time division multiple access (TDMA)network, global system for mobile communications (GSM) network), and/orother circuit-based networks.

Comprise, include, and/or plural forms of each are open ended andinclude the listed parts and can include additional parts that are notlisted. And/or is open ended and includes one or more of the listedparts and combinations of the listed parts.

One skilled in the art will realize the invention may be embodied inother specific forms without departing from the spirit or essentialcharacteristics thereof. The foregoing embodiments are therefore to beconsidered in all respects illustrative rather than limiting of theinvention described herein. Scope of the invention is thus indicated bythe appended claims, rather than by the foregoing description, and allchanges that come within the meaning and range of equivalency of theclaims are therefore intended to be embraced therein.

What is claimed is:
 1. A method for performing cellular communication ina Long Term Evolution (LTE) network, the method comprising: receiving arequest for a reference signal received power (RSRP) estimation from afirst base station; receiving a cellular communication signal y_(i) fromthe first base station and at least one interference base stationtransmitted over the LTE network, wherein y_(i)=[y_(0,i), . . . ,y_(N-1,i)]^(T), N is a size of an observation vector of the cellularcommunication signal, i is the i-th orthogonal frequency-divisionmultiplexing (OFDM) symbol, wherein the received cellular communicationsignal after a Fast Fourier Transform (FFT) in the i-th orthogonalfrequency-division multiplexing (OFDM symbol) is applied is defined byy_(i)=C_(0,i)F₀h_(0,i)+C_(1,i)F₁h_(1,i)+n_(i), wherein C_(0,i) is a Cellspecific Reference Signal for the target first base station, F₀ is amodified version of the FFT for the first base station, and h_(0,i) is achannel domain impulse response sampled at a bandwidth sample rate ofthe cellular communications signal for the first base station, C_(1,i)is a Cell specific Reference Signal for a second base station of the LTEnetwork, F₁ is a modified version of the FFT for the second basestation, and h_(1,i) is a channel domain impulse response sampled at thebandwidth sample rate of the cellular communications signal for thesecond base station; determining a first channel estimation of {tildeover (h)}_(1,i) the second base station of the LTE network based on thecellular communication signal, wherein {tilde over (h)}_(1,i)=F₁^(†)C_(1,i) ^(H)y_(i), wherein F₁ ^(†)=(F₁ ^(H)F₁)⁻¹F₁ ^(H), whereinC_(1,i) ^(H) the Cell specific Reference Signal for the second basestation of the LTE network and H is a Hermitian transpose operator;performing an interference cancellation Ic based on the cellularcommunication signal and the first channel estimation, whereinIc=y_(i)−C_(1,i)F₁{tilde over (h)}_(1,i); determining a second channelestimation {tilde over (h)}_(0,i) the first base station based on thecellular communication signal after interference cancellation, wherein{tilde over (h)}_(0,i)=F₀ ^(†)C_(0,i) ^(H)(I_(c)); determining a biasmatrix B_(i) based on the second channel estimation, wherein {tilde over(h)}_(0,i)=h_(0,i)−F₀ ^(†)C_(0,i) ^(H)F₁F₁ ^(\)C_(1,i)^(H)C_(0,i)F₀h_(0,i)+{dot over (n)}_(i), wherein F₀ ^(†) is the F₀^(†)=(F₀ ^(H)F₀)⁻¹F₀ ^(H), wherein C_(0,i) ^(H) is the Cell specificReference Signal for the second base station of the LTE network, and{dot over (n)}_(i) is a filtered noise term, wherein ĥ_(0,i) isexpressed as, ĥ_(0,i)=(I−B_(i))⁻¹ĥ_(0,i)=h_(0,i)+(I−B_(i))⁻¹{dot over(n)}_(i); determining RSRP estimation of the first base station based onthe second channel estimation and the bias, wherein RSRP=1/MΣ_(i=0)^(M-1)|F₀ĥ_(0,i)|²−gσ², wherein M is a predefined number of symbols inthe cellular communication signal, g is an expectation value of thefiltered noise term {dot over (n)}_(i), σ² is noise power, wherein RSRPis expressed asRSRP=1/MΣ _(i=0) ^(M-1) {tilde over (h)} _(0,1) ^(−H)(I−B _(i))^(−H) F ₀^(h) F ₀(I−B _(i))⁻¹ {tilde over (h)} _(0,i) −gσ ²; reporting the RSRPof the first base station; and managing cell handover based on the RSRP.2. The method of claim 1 wherein the bias is a bias matrix anddetermining the bias further comprises determining an expectation valueof the bias matrix.
 3. The method of claim 1 wherein the bias is a biasmatrix and determining the bias further comprises using a Neumann seriesapproximation.
 4. The method of claim 1 wherein determining the biasfurther comprises retrieving the bias from a computer memory.
 5. Themethod of claim 4 wherein determining the bias further comprisesmodifying the retrieved bias.
 6. The method of claim 1 whereindetermining the RSRP estimation is further based on a noise factor. 7.The method of claim 6 further comprising retrieving the noise factorfrom a computer memory.
 8. The method of claim 7 further comprisingmodifying the retrieved noise factor.
 9. The method of claim 1 whereinpilot symbols of the target cell and pilot symbols of the interferingcell overlap.
 10. The method of claim 1 wherein the target cell and theinterfering cell have an overlapping area of cellular coverage.
 11. Themethod of claim 1 wherein the cellular communication signal is anorthogonal frequency-division multiplexing (OFDM) signal.
 12. A methodfor performing cellular communication in a in Long Term Evolution (LTE)network, the method comprising: receiving a request for a referencesignal received power (RSRP) estimation from a target cell; receiving acellular communication signal y_(i) transmitted over the LTE network,wherein y_(i)=[y_(0,i), . . . , y_(N-1,i)]^(T), N is a size of anobservation vector of the cellular communication signal, i is the i-thorthogonal frequency-division multiplexing (OFDM) symbol, wherein thereceived cellular communication signal after the Fast Fourier Transform(FFT) in the i-th orthogonal frequency-division multiplexing (OFDMsymbol) is applied is defined byy_(i)=C_(0,i)F₀h_(0,i)+C_(1,i)F₁h_(1,i)+n_(i), wherein C_(0,i) is a Cellspecific Reference Signal for the target cell, F₀ is a modified versionof the FFT for the first base station, and h_(0,i) is a channel domainimpulse response sampled at a bandwidth sample rate of the cellularcommunications signal for the first base station, C_(1,i) is a Cellspecific Reference Signal for a second base station of the LTE network,F₁ is a modified version of the FFT for the second base station, andh_(1,i) is a channel domain impulse response sampled at the bandwidthsample rate of the cellular communications signal for the interferencecell; determining a first channel estimation of {tilde over (h)}_(1,i)of the interference cell, wherein {tilde over (h)}_(1,i)=F₁ ^(†)C_(1,i)^(H)y_(i), wherein F₁ ^(†)=(F₁ ^(H)F₁)⁻¹F₁ ^(H), wherein C_(1,i) ^(H)the Cell specific Reference Signal for the second base station of theLTE network and H is a Hermitian transpose operator; performing aninterference cancellation Ic, wherein Ic=y_(i)−C_(1,i)F₁{tilde over(h)}_(1,i); determining a second channel estimation {tilde over(h)}_(0,i) the interference cell, wherein {tilde over (h)}_(0,i)=F₀^(†)C_(0,i) ^(H)(I_(c)); determining a bias matrix B_(i), wherein {tildeover (h)}_(0,i)=h_(0,i)−F₀ ^(†)C_(0,i) ^(H)F₁F₁ ^(\)C_(1,i)^(H)C_(0,i)F₀h_(0,i)+{dot over (n)}_(i), wherein F₀ ^(†) is the F₀^(†)=(F₀ ^(H)F₀)⁻¹F₀ ^(H), wherein C_(0,i) ^(H) is the Cell specificReference Signal for an interference cell of the LTE network, and {dotover (n)}_(i) is a filtered noise term, wherein ĥ_(0,i) is expressed as,ĥ_(0,i)=(I−B_(i))⁻¹=h_(0,i)+(I−B_(i))⁻¹{dot over (n)}_(i); determiningRSRP estimation of the target cell, wherein${{RSRP} = {{\frac{1}{M}{\sum_{i = 0}^{M - 1}{{F_{0}{\hat{h}}_{0,i}}}^{2}}} - {g\;\sigma^{2}}}},$wherein M is a predefined number of symbols in the cellularcommunication signal, g is an expectation value of the filtered noiseterm {dot over (n)}_(i), σ² is noise power, wherein RSRP is expressed as${{RSRP} = {{\frac{1}{M}{\sum_{i = 0}^{M - 1}{{{\overset{\sim}{h}}_{0,1}^{H}\left( {I - B_{i}} \right)}^{- H}F_{0}^{h}{F_{0}\left( {I - B_{i}} \right)}^{- 1}{\overset{\sim}{h}}_{0,i}}}} - {g\;\sigma^{2}}}};$reporting the RSRP of the first base station.
 13. The method of claim 12further comprising: approximating (I−B_(i))^(−H) to (I+B_(i))^(H) basedon a first and second element of a Neumann series approximation ofB_(i).
 14. The method of claim 13 wherein determining the RSRP furthercomprises: determining an expectation value of B_(i) whereinB=E(B_(i))=F₀ ^(†)diag(F₁F₁ ^(†))F₀; and replacing B_(i) with theexpectation value such that,${RSRP} = {{\frac{1}{M}{\sum_{i = 0}^{M - 1}{\left( {{F_{0}^{H}F_{0}} + {2\;{{Re}\left( {{\overset{\_}{B}}^{H}F_{0}^{H}F_{0}} \right)}}} \right){\overset{\sim}{h}}_{0,i}}}} - {g\;{\sigma^{2}.}}}$15. The method of claim 12 further comprising ignoring B_(i) ^(H)F₀^(H)F₀B_(i).
 16. The method of claim 12 wherein determining theexpectation value of the filtered noise term further comprises:g=M·Tr(F ₀ ^(†H) GF ₀ ^(†)−2Re(F ₀ ^(†H) GF ₀ ^(†)diag(F ₁ F ₁^(†)))+M·Tr((F ₁ F ₁ ^(†))^(H)diag(F ₀ ^(†H) GF ₀ ^(†))F ₁ F ₁ ^(†))wherein G=F ₀ ^(H) F ₀+2Re( B ^(H) F ₀ ^(H) F ₀).